Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.

Multi-granularity physicochemical-inspired molecular representation learning for property prediction.

Integrating clinicopathologic information and dynamic contrast-enhanced MRI for augmented prediction of neoadjuvant chemotherapy response in breast cancer.

BTSSPro: Prompt-Guided Multimodal Co-Learning for Breast Cancer Tumor Segmentation and Survival Prediction.

A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations.

Sparse graph cascade multi-kernel fusion contrastive learning for microbe-disease association prediction.

PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation.

StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction.

Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges.

EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention.

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals.

Dual Network Contrastive Learning for Predicting Microbe-Disease Associations.

Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reaction.

Med-MMHL: A Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain.

DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks.

Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks.

Applying Bi-directional Link Mining in Personalized Recommendation.